from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase          =						{
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						["""RobertaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						[
	    """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaForCausalLM""",
	    """RobertaForMaskedLM""",
	    """RobertaForMultipleChoice""",
	    """RobertaForQuestionAnswering""",
	    """RobertaForSequenceClassification""",
	    """RobertaForTokenClassification""",
	    """RobertaModel""",
	    """RobertaPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						[
	    """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaForCausalLM""",
	    """TFRobertaForMaskedLM""",
	    """TFRobertaForMultipleChoice""",
	    """TFRobertaForQuestionAnswering""",
	    """TFRobertaForSequenceClassification""",
	    """TFRobertaForTokenClassification""",
	    """TFRobertaMainLayer""",
	    """TFRobertaModel""",
	    """TFRobertaPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						[
	    """FlaxRobertaForCausalLM""",
	    """FlaxRobertaForMaskedLM""",
	    """FlaxRobertaForMultipleChoice""",
	    """FlaxRobertaForQuestionAnswering""",
	    """FlaxRobertaForSequenceClassification""",
	    """FlaxRobertaForTokenClassification""",
	    """FlaxRobertaModel""",
	    """FlaxRobertaPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

	_UpperCAmelCase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase          =						logging.get_logger(__name__)

_UpperCAmelCase          =						{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def 							UpperCamelCase		(			__lowercase		:					int		,__lowercase		:					List[str]		,__lowercase		:					str		,__lowercase		:					Optional[Any]		,__lowercase		:					Any      ):
		'''simple docstring'''




		for attribute in key.split('.'      ):
				A_						:							Dict      = getattr(__lowercase		,__lowercase      )

		if weight_type is not None:
				A_						:							Any      = getattr(__lowercase		,__lowercase      ).shape
		else:
				A_						:							Optional[Any]      = hf_pointer.shape

		assert hf_shape == value.shape, (
		    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
		    f''' {value.shape} for {full_name}'''
		)

		if weight_type == "weight":
				A_						:							int      = value
		elif weight_type == "weight_g":
				A_						:							Tuple      = value
		elif weight_type == "weight_v":
				A_						:							Union[str, Any]      = value
		elif weight_type == "bias":
				A_						:							Any      = value
		else:
				A_						:							str      = value

		logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''      )




def 							UpperCamelCase		(			__lowercase		:					str		,__lowercase		:					Dict		,__lowercase		:					Tuple      ):
		'''simple docstring'''




		A_						:							Optional[Any]      = []
		A_						:							Tuple      = fairseq_model.state_dict()

		A_						:							Any      = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				A_						:							Union[str, Any]      = False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowercase		,__lowercase		,__lowercase		,__lowercase		,hf_model.config.feat_extract_norm == 'group'		,)
						A_						:							List[str]      = True
				else:
						for key, mapped_key in MAPPING.items():
								A_						:							str      = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

								if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
										A_						:							int      = True
										if "*" in mapped_key:
												A_						:							str      = name.split(__lowercase      )[0].split('.'      )[-2]
												A_						:							Optional[Any]      = mapped_key.replace('*'		,__lowercase      )
										if "weight_g" in name:
												A_						:							Dict      = 'weight_g'
										elif "weight_v" in name:
												A_						:							Tuple      = 'weight_v'
										elif "weight" in name:
												A_						:							Union[str, Any]      = 'weight'
										elif "bias" in name:
												A_						:							Optional[Any]      = 'bias'
										else:
												A_						:							Union[str, Any]      = None
										set_recursively(__lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase      )
								continue
				if not is_used:
						unused_weights.append(__lowercase      )

		logger.warning(f'''Unused weights: {unused_weights}'''      )




def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					Union[str, Any]		,__lowercase		:					Any		,__lowercase		:					List[Any]		,__lowercase		:					Union[str, Any]      ):
		'''simple docstring'''




		A_						:							Optional[int]      = full_name.split('conv_layers.'      )[-1]
		A_						:							Any      = name.split('.'      )
		A_						:							Dict      = int(items[0]      )
		A_						:							Optional[int]      = int(items[1]      )

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
						)
						A_						:							Optional[int]      = value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
						)
						A_						:							Union[str, Any]      = value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
						    " found."
						)
						A_						:							Any      = value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
						)
						A_						:							Tuple      = value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
		else:
				unused_weights.append(__lowercase      )




def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					str      ):
		'''simple docstring'''




		A_						:							Union[str, Any]      = SEWConfig()
		if is_finetuned:
				A_						:							Any      = model.wav_encoder.wav_model.cfg
		else:
				A_						:							int      = model.cfg

		A_						:							Any      = fs_config.conv_bias
		A_						:							Dict      = eval(fs_config.conv_feature_layers      )
		A_						:							List[Any]      = [x[0] for x in conv_layers]
		A_						:							Optional[Any]      = [x[1] for x in conv_layers]
		A_						:							List[Any]      = [x[2] for x in conv_layers]
		A_						:							Optional[int]      = 'gelu'
		A_						:							Union[str, Any]      = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		A_						:							Tuple      = 0.0
		A_						:							Dict      = fs_config.activation_fn.name
		A_						:							List[Any]      = fs_config.encoder_embed_dim
		A_						:							int      = 0.02
		A_						:							List[str]      = fs_config.encoder_ffn_embed_dim
		A_						:							Any      = 1e-5
		A_						:							Optional[Any]      = fs_config.encoder_layerdrop
		A_						:							Optional[int]      = fs_config.encoder_attention_heads
		A_						:							Any      = fs_config.conv_pos_groups
		A_						:							int      = fs_config.conv_pos
		A_						:							Tuple      = len(__lowercase      )
		A_						:							List[Any]      = fs_config.encoder_layers
		A_						:							Any      = fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				A_						:							Union[str, Any]      = model.cfg
				A_						:							str      = fs_config.final_dropout
				A_						:							Any      = fs_config.layerdrop
		A_						:							str      = fs_config.activation_dropout
		A_						:							Any      = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		A_						:							str      = fs_config.attention_dropout
		A_						:							Any      = fs_config.dropout_input
		A_						:							Dict      = fs_config.dropout
		A_						:							Optional[Any]      = fs_config.mask_channel_length
		A_						:							List[str]      = fs_config.mask_channel_prob
		A_						:							Tuple      = fs_config.mask_length
		A_						:							Dict      = fs_config.mask_prob

		A_						:							Any      = 'Wav2Vec2FeatureExtractor'
		A_						:							Union[str, Any]      = 'Wav2Vec2CTCTokenizer'

		return config




@torch.no_grad()
def 							UpperCamelCase		(			__lowercase		:					List[Any]		,__lowercase		:					int		,__lowercase		:					Optional[int]=None		,__lowercase		:					Optional[Any]=None		,__lowercase		:					str=True      ):
		'''simple docstring'''




		if is_finetuned:
				A_  ,     A_  ,     A_						:							List[Any]      = fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]		,arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
		else:
				A_  ,     A_  ,     A_						:							Union[str, Any]      = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

		if config_path is not None:
				A_						:							Union[str, Any]      = SEWConfig.from_pretrained(__lowercase      )
		else:
				A_						:							Dict      = convert_config(model[0]		,__lowercase      )
		A_						:							Union[str, Any]      = model[0].eval()

		A_						:							Optional[int]      = True if config.feat_extract_norm == 'layer' else False
		A_						:							List[Any]      = WavaVecaFeatureExtractor(
		    feature_size=1		,sampling_rate=1_60_00		,padding_value=0		,do_normalize=__lowercase		,return_attention_mask=__lowercase		,)

		if is_finetuned:
				if dict_path:
						A_						:							Optional[int]      = Dictionary.load(__lowercase      )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						A_						:							int      = target_dict.pad_index
						A_						:							List[Any]      = target_dict.bos_index
						A_						:							Optional[Any]      = target_dict.pad_index
						A_						:							str      = target_dict.bos_index
						A_						:							str      = target_dict.eos_index
						A_						:							str      = len(target_dict.symbols      )
						A_						:							Union[str, Any]      = os.path.join(__lowercase		,'vocab.json'      )
						if not os.path.isdir(__lowercase      ):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase      )      )
								return
						os.makedirs(__lowercase		,exist_ok=__lowercase      )
						with open(__lowercase		,'w'		,encoding='utf-8'      ) as vocab_handle:
								json.dump(target_dict.indices		,__lowercase      )
						A_						:							Any      = WavaVecaCTCTokenizer(
						    __lowercase		,unk_token=target_dict.unk_word		,pad_token=target_dict.pad_word		,bos_token=target_dict.bos_word		,eos_token=target_dict.eos_word		,word_delimiter_token='|'		,do_lower_case=__lowercase		,)
						A_						:							Tuple      = WavaVecaProcessor(feature_extractor=__lowercase		,tokenizer=__lowercase      )
						processor.save_pretrained(__lowercase      )

				A_						:							Dict      = SEWForCTC(__lowercase      )
		else:
				A_						:							Tuple      = SEWModel(__lowercase      )
				feature_extractor.save_pretrained(__lowercase      )

		recursively_load_weights(__lowercase		,__lowercase		,__lowercase      )

		hf_model.save_pretrained(__lowercase      )


if __name__ == "__main__":
	_UpperCAmelCase          =						argparse.ArgumentParser()
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
	parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
	parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
	parser.add_argument(
	    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
	)
	_UpperCAmelCase          =						parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)






